非表示:
キーワード:
-
要旨:
In this paper a novel representation for image classification is proposed which exploits the temporal
information inherent in natural visual input. Image sequences are represented by a set of salient features which
are found by tracking of visual features. In the context of a multi-class classification problem this representation is
compared against a representation using only raw image data. The dataset consists of image sequences generated
from a processed version of the MPI face database. We consider two types of multi-class SVMs and benchmark
them against nearest-neighbor classifiers. By introducing a new set of SVM kernel functions we show that the
feature representation significantly outperforms the view representation.
